"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

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* Redistributions of source code must retain the above copyright notice, this
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  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
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"""

import torch
import torch.nn as nn
from torch.nn import functional as F

from .layers import create_conv2d, drop_path, make_divisible, create_act_layer
from .layers.activations import sigmoid

__all__ = [
    'SqueezeExcite', 'ConvBnAct', 'DepthwiseSeparableConv', 'InvertedResidual', 'CondConvResidual', 'EdgeResidual']


class SqueezeExcite(nn.Module):
    """ Squeeze-and-Excitation w/ specific features for EfficientNet/MobileNet family

    Args:
        in_chs (int): input channels to layer
        rd_ratio (float): ratio of squeeze reduction
        act_layer (nn.Module): activation layer of containing block
        gate_layer (Callable): attention gate function
        force_act_layer (nn.Module): override block's activation fn if this is set/bound
        rd_round_fn (Callable): specify a fn to calculate rounding of reduced chs
    """

    def __init__(
            self, in_chs, rd_ratio=0.25, rd_channels=None, act_layer=nn.ReLU,
            gate_layer=nn.Sigmoid, force_act_layer=None, rd_round_fn=None):
        super(SqueezeExcite, self).__init__()
        if rd_channels is None:
            rd_round_fn = rd_round_fn or round
            rd_channels = rd_round_fn(in_chs * rd_ratio)
        act_layer = force_act_layer or act_layer
        self.conv_reduce = nn.Conv2d(in_chs, rd_channels, 1, bias=True)
        self.act1 = create_act_layer(act_layer, inplace=True)
        self.conv_expand = nn.Conv2d(rd_channels, in_chs, 1, bias=True)
        self.gate = create_act_layer(gate_layer)

    def forward(self, x):
        x_se = x.mean((2, 3), keepdim=True)
        x_se = self.conv_reduce(x_se)
        x_se = self.act1(x_se)
        x_se = self.conv_expand(x_se)
        return x * self.gate(x_se)


class ConvBnAct(nn.Module):
    """ Conv + Norm Layer + Activation w/ optional skip connection
    """
    def __init__(
            self, in_chs, out_chs, kernel_size, stride=1, dilation=1, pad_type='',
            skip=False, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, drop_path_rate=0.):
        super(ConvBnAct, self).__init__()
        self.has_residual = skip and stride == 1 and in_chs == out_chs
        self.drop_path_rate = drop_path_rate
        self.conv = create_conv2d(in_chs, out_chs, kernel_size, stride=stride, dilation=dilation, padding=pad_type)
        self.bn1 = norm_layer(out_chs)
        self.act1 = act_layer(inplace=True)

    def feature_info(self, location):
        if location == 'expansion':  # output of conv after act, same as block coutput
            info = dict(module='act1', hook_type='forward', num_chs=self.conv.out_channels)
        else:  # location == 'bottleneck', block output
            info = dict(module='', hook_type='', num_chs=self.conv.out_channels)
        return info

    def forward(self, x):
        shortcut = x
        x = self.conv(x)
        x = self.bn1(x)
        x = self.act1(x)
        if self.has_residual:
            if self.drop_path_rate > 0.:
                x = drop_path(x, self.drop_path_rate, self.training)
            x += shortcut
        return x


class DepthwiseSeparableConv(nn.Module):
    """ DepthwiseSeparable block
    Used for DS convs in MobileNet-V1 and in the place of IR blocks that have no expansion
    (factor of 1.0). This is an alternative to having a IR with an optional first pw conv.
    """
    def __init__(
            self, in_chs, out_chs, dw_kernel_size=3, stride=1, dilation=1, pad_type='',
            noskip=False, pw_kernel_size=1, pw_act=False, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
            se_layer=None, drop_path_rate=0.):
        super(DepthwiseSeparableConv, self).__init__()
        self.has_residual = (stride == 1 and in_chs == out_chs) and not noskip
        self.has_pw_act = pw_act  # activation after point-wise conv
        self.drop_path_rate = drop_path_rate

        self.conv_dw = create_conv2d(
            in_chs, in_chs, dw_kernel_size, stride=stride, dilation=dilation, padding=pad_type, depthwise=True)
        self.bn1 = norm_layer(in_chs)
        self.act1 = act_layer(inplace=True)

        # Squeeze-and-excitation
        self.se = se_layer(in_chs, act_layer=act_layer) if se_layer else nn.Identity()

        self.conv_pw = create_conv2d(in_chs, out_chs, pw_kernel_size, padding=pad_type)
        self.bn2 = norm_layer(out_chs)
        self.act2 = act_layer(inplace=True) if self.has_pw_act else nn.Identity()

    def feature_info(self, location):
        if location == 'expansion':  # after SE, input to PW
            info = dict(module='conv_pw', hook_type='forward_pre', num_chs=self.conv_pw.in_channels)
        else:  # location == 'bottleneck', block output
            info = dict(module='', hook_type='', num_chs=self.conv_pw.out_channels)
        return info

    def forward(self, x):
        shortcut = x

        x = self.conv_dw(x)
        x = self.bn1(x)
        x = self.act1(x)

        x = self.se(x)

        x = self.conv_pw(x)
        x = self.bn2(x)
        x = self.act2(x)

        if self.has_residual:
            if self.drop_path_rate > 0.:
                x = drop_path(x, self.drop_path_rate, self.training)
            x += shortcut
        return x


class InvertedResidual(nn.Module):
    """ Inverted residual block w/ optional SE

    Originally used in MobileNet-V2 - https://arxiv.org/abs/1801.04381v4, this layer is often
    referred to as 'MBConv' for (Mobile inverted bottleneck conv) and is also used in
      * MNasNet - https://arxiv.org/abs/1807.11626
      * EfficientNet - https://arxiv.org/abs/1905.11946
      * MobileNet-V3 - https://arxiv.org/abs/1905.02244
    """

    def __init__(
            self, in_chs, out_chs, dw_kernel_size=3, stride=1, dilation=1, pad_type='',
            noskip=False, exp_ratio=1.0, exp_kernel_size=1, pw_kernel_size=1, act_layer=nn.ReLU,
            norm_layer=nn.BatchNorm2d, se_layer=None, conv_kwargs=None, drop_path_rate=0.):
        super(InvertedResidual, self).__init__()
        conv_kwargs = conv_kwargs or {}
        mid_chs = make_divisible(in_chs * exp_ratio)
        self.has_residual = (in_chs == out_chs and stride == 1) and not noskip
        self.drop_path_rate = drop_path_rate

        # Point-wise expansion
        self.conv_pw = create_conv2d(in_chs, mid_chs, exp_kernel_size, padding=pad_type, **conv_kwargs)
        self.bn1 = norm_layer(mid_chs)
        self.act1 = act_layer(inplace=True)

        # Depth-wise convolution
        self.conv_dw = create_conv2d(
            mid_chs, mid_chs, dw_kernel_size, stride=stride, dilation=dilation,
            padding=pad_type, depthwise=True, **conv_kwargs)
        self.bn2 = norm_layer(mid_chs)
        self.act2 = act_layer(inplace=True)

        # Squeeze-and-excitation
        self.se = se_layer(mid_chs, act_layer=act_layer) if se_layer else nn.Identity()

        # Point-wise linear projection
        self.conv_pwl = create_conv2d(mid_chs, out_chs, pw_kernel_size, padding=pad_type, **conv_kwargs)
        self.bn3 = norm_layer(out_chs)

    def feature_info(self, location):
        if location == 'expansion':  # after SE, input to PWL
            info = dict(module='conv_pwl', hook_type='forward_pre', num_chs=self.conv_pwl.in_channels)
        else:  # location == 'bottleneck', block output
            info = dict(module='', hook_type='', num_chs=self.conv_pwl.out_channels)
        return info

    def forward(self, x):
        shortcut = x

        # Point-wise expansion
        x = self.conv_pw(x)
        x = self.bn1(x)
        x = self.act1(x)

        # Depth-wise convolution
        x = self.conv_dw(x)
        x = self.bn2(x)
        x = self.act2(x)

        # Squeeze-and-excitation
        x = self.se(x)

        # Point-wise linear projection
        x = self.conv_pwl(x)
        x = self.bn3(x)

        if self.has_residual:
            if self.drop_path_rate > 0.:
                x = drop_path(x, self.drop_path_rate, self.training)
            x += shortcut

        return x


class CondConvResidual(InvertedResidual):
    """ Inverted residual block w/ CondConv routing"""

    def __init__(
            self, in_chs, out_chs, dw_kernel_size=3, stride=1, dilation=1, pad_type='',
            noskip=False, exp_ratio=1.0, exp_kernel_size=1, pw_kernel_size=1, act_layer=nn.ReLU,
            norm_layer=nn.BatchNorm2d, se_layer=None, num_experts=0, drop_path_rate=0.):

        self.num_experts = num_experts
        conv_kwargs = dict(num_experts=self.num_experts)

        super(CondConvResidual, self).__init__(
            in_chs, out_chs, dw_kernel_size=dw_kernel_size, stride=stride, dilation=dilation, pad_type=pad_type,
            act_layer=act_layer, noskip=noskip, exp_ratio=exp_ratio, exp_kernel_size=exp_kernel_size,
            pw_kernel_size=pw_kernel_size, se_layer=se_layer, norm_layer=norm_layer, conv_kwargs=conv_kwargs,
            drop_path_rate=drop_path_rate)

        self.routing_fn = nn.Linear(in_chs, self.num_experts)

    def forward(self, x):
        shortcut = x

        # CondConv routing
        pooled_inputs = F.adaptive_avg_pool2d(x, 1).flatten(1)
        routing_weights = torch.sigmoid(self.routing_fn(pooled_inputs))

        # Point-wise expansion
        x = self.conv_pw(x, routing_weights)
        x = self.bn1(x)
        x = self.act1(x)

        # Depth-wise convolution
        x = self.conv_dw(x, routing_weights)
        x = self.bn2(x)
        x = self.act2(x)

        # Squeeze-and-excitation
        x = self.se(x)

        # Point-wise linear projection
        x = self.conv_pwl(x, routing_weights)
        x = self.bn3(x)

        if self.has_residual:
            if self.drop_path_rate > 0.:
                x = drop_path(x, self.drop_path_rate, self.training)
            x += shortcut
        return x


class EdgeResidual(nn.Module):
    """ Residual block with expansion convolution followed by pointwise-linear w/ stride

    Originally introduced in `EfficientNet-EdgeTPU: Creating Accelerator-Optimized Neural Networks with AutoML`
        - https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html

    This layer is also called FusedMBConv in the MobileDet, EfficientNet-X, and EfficientNet-V2 papers
      * MobileDet - https://arxiv.org/abs/2004.14525
      * EfficientNet-X - https://arxiv.org/abs/2102.05610
      * EfficientNet-V2 - https://arxiv.org/abs/2104.00298
    """

    def __init__(
            self, in_chs, out_chs, exp_kernel_size=3, stride=1, dilation=1, pad_type='',
            force_in_chs=0, noskip=False, exp_ratio=1.0, pw_kernel_size=1, act_layer=nn.ReLU,
            norm_layer=nn.BatchNorm2d, se_layer=None, drop_path_rate=0.):
        super(EdgeResidual, self).__init__()
        if force_in_chs > 0:
            mid_chs = make_divisible(force_in_chs * exp_ratio)
        else:
            mid_chs = make_divisible(in_chs * exp_ratio)
        has_se = se_layer is not None and se_ratio > 0.
        self.has_residual = (in_chs == out_chs and stride == 1) and not noskip
        self.drop_path_rate = drop_path_rate

        # Expansion convolution
        self.conv_exp = create_conv2d(
            in_chs, mid_chs, exp_kernel_size, stride=stride, dilation=dilation, padding=pad_type)
        self.bn1 = norm_layer(mid_chs)
        self.act1 = act_layer(inplace=True)

        # Squeeze-and-excitation
        self.se = se_layer(mid_chs, act_layer=act_layer) if se_layer else nn.Identity()

        # Point-wise linear projection
        self.conv_pwl = create_conv2d(mid_chs, out_chs, pw_kernel_size, padding=pad_type)
        self.bn2 = norm_layer(out_chs)

    def feature_info(self, location):
        if location == 'expansion':  # after SE, before PWL
            info = dict(module='conv_pwl', hook_type='forward_pre', num_chs=self.conv_pwl.in_channels)
        else:  # location == 'bottleneck', block output
            info = dict(module='', hook_type='', num_chs=self.conv_pwl.out_channels)
        return info

    def forward(self, x):
        shortcut = x

        # Expansion convolution
        x = self.conv_exp(x)
        x = self.bn1(x)
        x = self.act1(x)

        # Squeeze-and-excitation
        x = self.se(x)

        # Point-wise linear projection
        x = self.conv_pwl(x)
        x = self.bn2(x)

        if self.has_residual:
            if self.drop_path_rate > 0.:
                x = drop_path(x, self.drop_path_rate, self.training)
            x += shortcut

        return x
